Knowledge-Enhanced Multi-task Learning for Course Recommendation

Qimin Ban, Wen Wu, Wenxin Hu, Hui Lin, Wei Zheng, Liang He

Abstract: Knowledge tracing (KT) aims to model learners’ knowledge level and predict future performance given their past interactions in learning applications. Adaptive learning systems mainly generate course recommendations based on learner’s knowledge level acquired by KT. However, for KT tasks, learners’ forgetting has not been well modeled. In addition, learner’s individual differences also influence the accuracy of knowledge level prediction. While for recommendation tasks, most of methods are conducted separately from KT tasks, ignoring the deep connection between them. In this paper, we are motivated to propose a Knowledge-Enhanced Multi-task Learning model for Course Recomme-ndation (KMCR), which regards the improved knowledge tracing task (IKTT) as an auxiliary task to assist the primary course recommendation task (CRT). Specifically, in IKTT, for assessing dynamic evolving knowledge level, we not only design a personalized controller to enhance the deep knowledge tracing model for modeling learner’s forgetting behavior, but also use personality to model the individual differences based on the theory of cognitive psychology. In CRT, we adaptively combine learner’s knowledge level obtained by IKTT with their sequential behavior to generate learners’ representation. The experimental results on real-world datasets demonstrate that our approach outperforms related methods in terms of recommendation accuracy.